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'use strict';
import {
get_size,
parse_genome,
mut_gene_source,
mut_gene_sink,
mut_gene_weight,
} from './genome';
test('genome validation and size', () => {
expect(get_size(0, 0, [ [ 0, 0, 1.0 ] ])).toBe(1);
expect(get_size(2, 1, [ [ 0, 2, 1 ] ])).toBe(3);
expect(get_size(2, 1, [ [ 0, 1, 1 ] ])).toBe(-1);
expect(get_size(2, 1, [ [ 0, 2, 5 ] ])).toBe(-1);
});
test('parse a genome into a neural net', () => {
const n = parse_genome(1, 1, [
[0, 1, 1],
[1, 1, 1],
[1, 2, 1]
]);
expect(n.input_count).toBe(1);
expect(n.output_count).toBe(1);
expect(n.compute([2], [-1])).toEqual([
[ Math.tanh( Math.tanh( 2-1 ) ) ],
[ Math.tanh( 2-1 ) ],
]);
});
test('mutate gene source', () => {
const n_input = 3;
const n_internal = 4;
const n_output = 5;
expect(mut_gene_source(
n_input, n_internal, n_output,
[0, 4, 0],
0.0
)).toEqual([0, 4, 0]);
expect(mut_gene_source(
n_input, n_internal, n_output,
[0, 4, 0],
1.0
)).toEqual([1, 4, 0]);
expect(mut_gene_source(
n_input, n_internal, n_output,
[6, 4, 0],
0.0
)).toEqual([5, 4, 0]);
expect(mut_gene_source(
n_input, n_internal, n_output,
[6, 4, 0],
1.0
)).toEqual([6, 4, 0]);
});
test('mutate gene sink', () => {
const n_input = 3;
const n_internal = 4;
const n_output = 5;
expect(mut_gene_sink(
n_input, n_internal, n_output,
[0, 7, 0],
0.0
)).toEqual([0, 7, 0]);
expect(mut_gene_sink(
n_input, n_internal, n_output,
[0, 7, 0],
1.0
)).toEqual([0, 8, 0]);
expect(mut_gene_sink(
n_input, n_internal, n_output,
[6, 11, 0],
0.0
)).toEqual([6, 10, 0]);
expect(mut_gene_sink(
n_input, n_internal, n_output,
[6, 11, 0],
1.0
)).toEqual([6, 11, 0]);
});
test('mutate gene weight', () => {
const weight_max = 4.0;
expect(mut_gene_weight(
weight_max, [0, 0, 1], 0.0
)).toEqual([0, 0, (2 - 4)/3]);
expect(mut_gene_weight(
weight_max, [0, 0, -4], 1.0
)).toEqual([0, 0, (-8 + 4)/3]);
expect(mut_gene_weight(
weight_max, [0, 0, 3], 0.5
)).toEqual([0, 0, (6+0)/3]);
});
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